[R-sig-ME] how to look at the effect of a variable I need to control for
glenda mendieta
glendamendieta at gmail.com
Fri Nov 18 17:13:19 CET 2011
Dear list members:
a while ago I made a consultation about the use of GLMM's that can be
found here:
https://stat.ethz.ch/pipermail/r-sig-mixed-models/2011q4/006873.html
I know there is a lot going on in the list for every consultation to be
answered, but, this time I have "simpler" question:
I have a doubt concerning a factor I want to see the effect from, but I
also need to control for.
My data consists on:
5 *census* in 10 years, each time we inspect for abundance of species
(*spp*) occurring on different individuals of a unique species of *tree*
(plots).
-census: 5 levels, as Fixed effect, since I want to see the effect of
time in the change of pres.abs or abundance of species
-trees: ~89 to 113, each individual tree inspected, as Ran.Eff., since I
hoped to control for temporal correlation, as we revise the same trees
every census
-spp: 89, number per species of epiphytes growing on the trees
-abs.pres: absence presence data of species growing on trees per census
(derived form count data), as ResVar
-avail.surface: surface in m2 per tree per census, as FE
in the following model, and with the above mentioned data, I would like
to test for the effect of time and surface availability on colonization
(absence/presence). My problem is that I don't know how to combine the
fact that the data are temporally correlated and control for that but
still look at the effect of time in absence and presence of species.
I tried placing time as a centered continuous variable as fixed effect
"c.census", and then again, as random effect, but as a factor in
(census|tree) or would be enough as: (1|tree), since the trees are the
ones being inspected every time?
glmm.all<-glmer(abs.pres~c.census*avail.surface+ (census|tree),
data=db.e_St, family=binomial(link=logit))
I would very much appreciate a hint on this since I got stuck with it
and can not seem to find my way around it.
thank you very much for your time in advance,
glenda mendieta-leiva
PhD candidate
University of Oldenburg, Germany
Smithsonian Tropical research institute
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